Melt pool monitoring techniques aid in the quality assurance and control of directed energy deposition (DED) additive manufacturing. Typically, the monitoring is based on the characterization of melt pool geometries, such as width, height, and depth. Among these, the melt pool depth cannot be measured directly. However, it indicates the distance from the deposited surface to the deepest point of the melt pool and is a key factor that determines the metallurgical bond between layers. In this study, an online melt pool depth estimation technique was developed for the DED process using a coaxial infrared (IR) camera, a laser line scanner, and an artificial neural network (ANN). Initially, the width and length of the melt pool at a particular position were measured using the coaxial IR camera. Simultaneously, the laser line scanner measured the build height and deposited track profile of the same position online. Features extracted from these measurements were used as inputs to the ANN model, and the melt pool depth was estimated online during multi-layer and multi-track printing. The performance of the proposed technique was verified considering multiple values of laser power, scanning speed, build height, and hatch spacing. The estimation results were compared with those obtained from optical microscopy inspection. The overall accuracy of the melt pool depth estimation was approximately 25.97 µm. These results demonstrate the effectiveness and potential of the proposed online melt pool depth estimation technique for DED process monitoring.

熔池监测技术有助于定向能量沉积 (DED) 增材制造的质量保证和控制。通常，监测基于熔池几何形状的特征，例如宽度、高度和深度。其中，熔池深度不能直接测量。然而，它表示从沉积表面到熔池最深点的距离，是决定层间冶金结合的关键因素。在这项研究中，使用同轴红外 (IR) 相机、激光线扫描仪和人工神经网络 (ANN) 为 DED 过程开发了一种在线熔池深度估计技术。最初，使用同轴红外相机测量特定位置熔池的宽度和长度。同时，激光线扫描仪在线测量同一位置的构建高度和沉积轨迹轮廓。从这些测量中提取的特征被用作 ANN 模型的输入，并且在多层和多轨道打印期间在线估计熔池深度。考虑到激光功率、扫描速度、构建高度和舱口间距的多个值，验证了所提出技术的性能。将估计结果与从光学显微镜检查中获得的结果进行比较。熔池深度估计的整体精度约为 25.97 µm。这些结果证明了所提出的用于 DED 过程监控的在线熔池深度估计技术的有效性和潜力。从这些测量中提取的特征被用作 ANN 模型的输入，并且在多层和多轨道打印期间在线估计熔池深度。考虑到激光功率、扫描速度、构建高度和舱口间距的多个值，验证了所提出技术的性能。将估计结果与从光学显微镜检查中获得的结果进行比较。熔池深度估计的整体精度约为 25.97 µm。这些结果证明了所提出的用于 DED 过程监控的在线熔池深度估计技术的有效性和潜力。从这些测量中提取的特征被用作 ANN 模型的输入，并且在多层和多轨道打印期间在线估计熔池深度。考虑到激光功率、扫描速度、构建高度和舱口间距的多个值，验证了所提出技术的性能。将估计结果与从光学显微镜检查中获得的结果进行比较。熔池深度估计的整体精度约为 25.97 µm。这些结果证明了所提出的用于 DED 过程监控的在线熔池深度估计技术的有效性和潜力。扫描速度、构建高度和舱口间距。将估计结果与从光学显微镜检查中获得的结果进行比较。熔池深度估计的整体精度约为 25.97 µm。这些结果证明了所提出的用于 DED 过程监控的在线熔池深度估计技术的有效性和潜力。扫描速度、构建高度和舱口间距。将估计结果与从光学显微镜检查中获得的结果进行比较。熔池深度估计的整体精度约为 25.97 µm。这些结果证明了所提出的用于 DED 过程监控的在线熔池深度估计技术的有效性和潜力。